AI is everywhere in revenue conversations right now. Leaders feel pressure to “do something with AI,” compare models, and chase the next big release.
Dan Geller, Chief AI Officer and Co-Founder of SherpasDurden, sees a different issue. “Revenue teams are asking the wrong question. They’re focused on which AI is best versus asking themselves, have we built the right foundation?”
For Dan, he sees two core problems: over-reliance on crowdsourced AI for strategy and under-investment in the behaviors and data that make AI worth using in the first place.
He highlights four shifts high-performing teams are making:
Stop Treating Public AI as a Strategy Engine
Public AI tools like ChatGPT work like Wikipedia: great for quick research and drafting, but not for building strategy. These tools are trained on generic internet content, not your business reality. When every competitor can get the same answers, there’s no competitive edge.
Smart teams treat public AI as assistants for execution—speeding up research, drafting first versions, exploring ideas. But strategic decisions come from verified sources: customer interviews, win/loss data, and proven messaging frameworks.
Design AI Around Value Pathways, Not Features
Most companies train teams on “how to use AI tools.” That’s like teaching PowerPoint without teaching presentation skills. Dan’s approach starts with the buyer journey: what do buyers need to know at each decision stage, by role, by industry, by pain point?
For revenue teams, this means reps use AI to deliver personalized outreach based on where the buyer is in their journey. A CFO early in research gets different messaging than an operations VP ready to evaluate vendors. AI helps execute that personalization at scale, but the value pathway comes first.
Build Personalization at Scale on Top of Verified Data
Public AI doesn’t know your best customer stories, your competitive wins, or what messaging actually converts. High-performing teams build private AI systems fed by their own data: case studies organized by use case and buyer segment, win/loss analysis, successful email templates, pitch decks that close deals.
Dan points to results: “One client cut discovery prep by 60% and improved message relevance—just by feeding AI their proven case studies, organized by industry.” The result: reps personalize at scale using content that’s already been validated by real customers.
Build AI Systems That Scale Business Expertise, Not Replace It
Many organizations fear AI will commoditize their services. Dan sees the opposite: using AI to scale what makes each business unique. He helps brands and agencies capture their intellectual property—frameworks, proven messaging strategies, campaign structures—and build AI systems that let teams execute at senior-level quality consistently.
For businesses focused on buyer personalization, this means AI helps deliver tailored value propositions, craft role-specific messaging, and build personalized content hierarchies faster—without diluting the strategic thinking that drives results.
For Dan, the question isn’t “Which AI model is winning?” It’s “Have we built the behaviors, data foundation, and value pathways that let any AI actually improve how we sell?”
The teams that answer “yes” aren’t just adding a new tool. They’re turning AI into a durable advantage—one that amplifies buyer personalization rather than replacing the strategic thinking behind real growth.